where \(\mathcal {U}_{\sigma ^{\prime }}\)  indicates upsampling the resolution of the output with the factor of \(\sigma ^{\prime }\) .\(\Theta _{p}\)  denotes the learnable parameters in \(\varphi \) .\(C_{o}\)  is the number of the prediction (, categories and auxiliary information). For example, in PSENet [1]}, \(\textbf {Q}\)  consists of \(C_{o}\)  segmentation masks for the text instances at different shrunk scales. It is worth noting that PSENet-1s (\(\sigma ^{\prime }\) /\(\sigma \)  = 4/4 = 1) achieves better performance than PSENet-4s (\(\sigma ^{\prime }\) /\(\sigma \)  = 1/4), due to different resolutions of the outputs. Similarly, \(\textbf {Q}\)  in DB [2]} contains the predicted shrunk text region map and the estimated threshold map, and its resolution is also upsampled to the same as that of the network input (\(\sigma ^{\prime }\) /\(\sigma \)  = 4/4 = 1) (More examples can be seen in Section  of Appendix). For a fair comparison, we set \(\sigma ^{\prime }\)  = \(\sigma \)  in experiments.
